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Optics Express

Optics Express

  • Editor: C. Martijn de Sterke
  • Vol. 15, Iss. 24 — Nov. 26, 2007
  • pp: 15747–15758

Speckle noise reduction algorithm for optical coherence tomography based on interval type II fuzzy set

Prabakar Puvanathasan and Kostadinka Bizheva  »View Author Affiliations

Optics Express, Vol. 15, Issue 24, pp. 15747-15758 (2007)

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A novel speckle reduction technique based on soft thresholding of wavelet coefficients using interval type II fuzzy system was developed for reducing speckle noise in Optical Coherence Tomography images. The proposed algorithm is an extension of a recently published method for filtering additive Gaussian noise by use of type I fuzzy system. Unlike type I, interval type II fuzzy based thresholding filter considers the uncertainty in the calculated threshold and the wavelet coefficient is adjusted based on this uncertainty. A single parameter controls the signal-to-noise (SNR) improvement. Application of this novel algorithm to optical coherence tomograms acquired in-vivo from a human finger tip show reduction in the speckle noise with little edge blurring and image SNR improvement of about 10dB. Comparison with adaptive Wiener and adaptive Lee filters, applied to the same image, demonstrated the superior performance of the fuzzy type II algorithm in terms of image metrics improvement.

© 2007 Optical Society of America

OCIS Codes
(030.6140) Coherence and statistical optics : Speckle
(100.0100) Image processing : Image processing
(100.7410) Image processing : Wavelets
(170.4500) Medical optics and biotechnology : Optical coherence tomography
(100.3008) Image processing : Image recognition, algorithms and filters

ToC Category:
Image Processing

Original Manuscript: October 1, 2007
Revised Manuscript: November 6, 2007
Manuscript Accepted: November 6, 2007
Published: November 12, 2007

Virtual Issues
Vol. 2, Iss. 12 Virtual Journal for Biomedical Optics

Prabakar Puvanathasan and Kostadinka Bizheva, "Speckle noise reduction algorithm for optical coherence tomography based on interval type II fuzzy set," Opt. Express 15, 15747-15758 (2007)

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